from typing import Literal, Optional, Union

import litellm
from litellm._logging import verbose_proxy_logger
from litellm.caching.caching import DualCache
from litellm.integrations.custom_guardrail import (
    CustomGuardrail,
    log_guardrail_information,
)
from litellm.proxy._types import UserAPIKeyAuth


class myCustomGuardrail(CustomGuardrail):
    def __init__(
        self,
        **kwargs,
    ):
        # store kwargs as optional_params
        self.optional_params = kwargs

        super().__init__(**kwargs)

    @log_guardrail_information
    async def async_pre_call_hook(
        self,
        user_api_key_dict: UserAPIKeyAuth,
        cache: DualCache,
        data: dict,
        call_type: Literal[
            "completion",
            "text_completion",
            "embeddings",
            "image_generation",
            "moderation",
            "audio_transcription",
            "pass_through_endpoint",
            "rerank",
            "mcp_call",
            "anthropic_messages",
        ],
    ) -> Optional[Union[Exception, str, dict]]:
        """
        Runs before the LLM API call
        Runs on only Input
        Use this if you want to MODIFY the input
        """

        # In this guardrail, if a user inputs `litellm` we will mask it and then send it to the LLM
        _messages = data.get("messages")
        if _messages:
            for message in _messages:
                _content = message.get("content")
                if isinstance(_content, str):
                    if "litellm" in _content.lower():
                        _content = _content.replace("litellm", "********")
                        message["content"] = _content

        verbose_proxy_logger.debug(
            "async_pre_call_hook: Message after masking %s", _messages
        )

        return data

    @log_guardrail_information
    async def async_moderation_hook(
        self,
        data: dict,
        user_api_key_dict: UserAPIKeyAuth,
        call_type: Literal[
            "completion",
            "embeddings",
            "image_generation",
            "moderation",
            "audio_transcription",
            "responses",
            "mcp_call",
            "anthropic_messages",
        ],
    ):
        """
        Runs in parallel to LLM API call
        Runs on only Input

        This can NOT modify the input, only used to reject or accept a call before going to LLM API
        """

        # this works the same as async_pre_call_hook, but just runs in parallel as the LLM API Call
        # In this guardrail, if a user inputs `litellm` we will mask it.
        _messages = data.get("messages")
        if _messages:
            for message in _messages:
                _content = message.get("content")
                if isinstance(_content, str):
                    if "litellm" in _content.lower():
                        raise ValueError("Guardrail failed words - `litellm` detected")

    @log_guardrail_information
    async def async_post_call_success_hook(
        self,
        data: dict,
        user_api_key_dict: UserAPIKeyAuth,
        response,
    ):
        """
        Runs on response from LLM API call

        It can be used to reject a response

        If a response contains the word "coffee" -> we will raise an exception
        """
        verbose_proxy_logger.debug("async_pre_call_hook response: %s", response)
        if isinstance(response, litellm.ModelResponse):
            for choice in response.choices:
                if isinstance(choice, litellm.Choices):
                    verbose_proxy_logger.debug("async_pre_call_hook choice: %s", choice)
                    if (
                        choice.message.content
                        and isinstance(choice.message.content, str)
                        and "coffee" in choice.message.content
                    ):
                        raise ValueError("Guardrail failed Coffee Detected")
